This series of files compile analyses done for the specific analysis
of Chapter 1, for the regional campaign of 2016.
All analyses have been done with PRIMER-e 6 and R 4.1.2.
Click on the table of contents in the left margin to assess a
specific analysis.
Click on a figure to zoom it
⏪
| 🏠
We used data from subtidal ecosystems (see metadata files for more
information). Only stations that have been sampled both for
abiotic parameters and benthic species were included.
Selected variables for the analyses:
- Depth of the station: depth (only for ANCOVAs)
- Percentage of organic matter: om
- Percentage of gravel: gravel
- Percentage of sand: sand
- Percentage of silt: silt
- Percentage of clay: clay
- Concentration of arsenic: arsenic
- Concentration of cadmium: cadmium
- Concentration of chromium: chromium
- Concentration of copper: copper
- Concentration of iron: iron
- Concentration of manganese: manganese
- Concentration of mercury: mercury
- Concentration of lead: lead
- Concentration of zinc: zinc
- Specific richness: S
- Total density of individuals: N
- Shannon’s diversity: H
- Piélou’s evenness: J
Abundances of Mesodesma arctatum (Marc) and
Cistenides granulata (Cgra) were also
considered (see IndVal and SIMPER results).
As data is missing for metal concentrations outside BSI, two
Designs have been used:
- Design 1: stations at BSI, CPC, BDA, MR with habitat parameters
- Design 2: stations at BSI with heavy metal concentrations.
1. Permutational Analyses of Covariance
Results of univariate PermANCOVAs on parameters and multivariate
PermANCOVA on the whole benthic community with depth as covariate are
presented in the table below. Variables have been standardized by mean
and standard-deviation, and taxon densities were (log+1)
transformed.
| om |
|
S |
S |
{CPC BDA MR} |
| gravel |
|
|
|
All regions in the same group |
| sand |
|
|
S |
All regions in the same group |
| silt |
S |
|
S |
{BSI CPC BDA}, {BDA MR} |
| clay |
|
|
|
{BSI BDA MR}, {CPC MR} |
| S (1 mm) |
|
|
S |
{BSI CPC MR}, {CPC BDA MR} |
| N (1 mm) |
|
|
|
All regions in the same group |
| H (1 mm) |
|
s~ |
S |
{CPC BDA MR}, {BSI MR} |
| J (1 mm) |
|
|
|
{BSI CPC MR}, {CPC BDA MR} |
| ALL SPECIES (1 mm) |
|
S |
S |
|
2. Similarity and characteristic species
Let’s have a look at the \(\beta\)
diversity within our conditions and sites.
Results of the PERMDISP routine are shown below (mean and SE of the
deviation from centroid for each group, i.e. multivariate
dispersion), along with the mean Bray-Curtis dissimilarity for each
group. Taxon densities were (log+1) transformed and PRIMER was used to
do the PERMDISP.
Mean within-group Bray-Curtis dissimilarity for each condition
or site
| HI |
64.6 |
0.83 |
0.917 |
| R |
61.9 |
1.14 |
0.878 |
| BSI |
62.9 |
1.18 |
0.903 |
| CPC |
60.2 |
2.25 |
0.87 |
| BDA |
61.1 |
1.93 |
0.882 |
| MR |
58.2 |
2.12 |
0.835 |
No significative relationships were found for either factor by the
PERMDISP (p = 0.069) or the pairwise tests.
The following analyses allowed to detect species as characteristic of
each condition. We used results from PRIMER to justify further their
choice.
## cluster indicator_value probability
## cistenides_granulata 1 0.2836 0.018
## macoma_calcarea 1 0.2326 0.002
## ennucula_tenuis 1 0.1860 0.018
## eudorellopsis_integra 1 0.1395 0.029
## mesodesma_arctatum 2 0.2342 0.007
## harmothoe_imbricata 2 0.1975 0.010
## glycera_alba 2 0.1212 0.039
## psammonyx_nobilis 2 0.1212 0.029
##
## Sum of probabilities = 50.871
##
## Sum of Indicator Values = 5.89
##
## Sum of Significant Indicator Values = 1.52
##
## Number of Significant Indicators = 8
##
## Significant Indicator Distribution
##
## 1 2
## 4 4
SIMPER results (mean Bray-Curtis between-group dissimilarity:
0.926)
| echinarachnius_parma |
0.0984 |
0.136 |
0.721 |
0.689 |
0.42 |
0.106 |
| mesodesma_arctatum |
0.07 |
0.129 |
0.542 |
0.605 |
0.0995 |
0.182 |
| cistenides_granulata |
0.0609 |
0.0948 |
0.643 |
0.176 |
0.565 |
0.248 |
| strongylocentrotus_sp |
0.0427 |
0.0758 |
0.563 |
0.27 |
0.249 |
0.294 |
| nephtys_caeca |
0.0425 |
0.0556 |
0.764 |
0.359 |
0.23 |
0.34 |
| limecola_balthica |
0.0313 |
0.0578 |
0.542 |
0.234 |
0.18 |
0.373 |
| scoloplos_armiger |
0.0295 |
0.065 |
0.453 |
0.14 |
0.256 |
0.405 |
| macoma_calcarea |
0.0274 |
0.0569 |
0.482 |
0 |
0.312 |
0.435 |
| harmothoe_imbricata |
0.0257 |
0.0583 |
0.44 |
0.217 |
0.0161 |
0.462 |
| amphipholis_squamata |
0.0238 |
0.0611 |
0.389 |
0.042 |
0.241 |
0.488 |
| protomedeia_grandimana |
0.0228 |
0.0538 |
0.424 |
0.183 |
0.169 |
0.513 |
| psammonyx_nobilis |
0.0189 |
0.0592 |
0.32 |
0.185 |
0 |
0.533 |
| thyasira_sp |
0.0186 |
0.0469 |
0.397 |
0.021 |
0.241 |
0.553 |
| ennucula_tenuis |
0.0185 |
0.0422 |
0.438 |
0 |
0.241 |
0.573 |
| mya_arenaria |
0.0174 |
0.034 |
0.513 |
0.063 |
0.168 |
0.592 |
| ciliatocardium_ciliatum |
0.014 |
0.045 |
0.312 |
0.0908 |
0.0766 |
0.607 |
| goniada_maculata |
0.0139 |
0.0354 |
0.391 |
0.021 |
0.173 |
0.622 |
| glycera_dibranchiata |
0.0134 |
0.043 |
0.31 |
0.021 |
0.0806 |
0.637 |
| glycera_alba |
0.0128 |
0.0408 |
0.313 |
0.172 |
0 |
0.65 |
| ameritella_agilis |
0.0117 |
0.0491 |
0.238 |
0 |
0.131 |
0.663 |
| astarte_undata |
0.0117 |
0.0388 |
0.301 |
0.142 |
0 |
0.676 |
| astarte_subaequilatera |
0.0106 |
0.0363 |
0.293 |
0.134 |
0 |
0.687 |
| nucula_proxima |
0.00992 |
0.0349 |
0.284 |
0 |
0.112 |
0.698 |
| pygospio_elegans |
0.00989 |
0.0449 |
0.22 |
0.137 |
0.0161 |
0.708 |
| ophelia_limacina |
0.00977 |
0.0299 |
0.327 |
0.042 |
0.0578 |
0.719 |
| diastylis_sculpta |
0.00966 |
0.0405 |
0.238 |
0.0488 |
0.0322 |
0.729 |
| eudorellopsis_integra |
0.00955 |
0.0267 |
0.358 |
0 |
0.153 |
0.74 |
| ampharetidae_spp |
0.00948 |
0.0277 |
0.342 |
0.0753 |
0.0535 |
0.75 |
| yoldia_myalis |
0.00913 |
0.0285 |
0.321 |
0.0543 |
0.0484 |
0.76 |
| nephtys_bucera |
0.00905 |
0.0256 |
0.354 |
0.063 |
0.0322 |
0.77 |
| ampeliscidae_spp |
0.00898 |
0.0253 |
0.354 |
0.063 |
0.0511 |
0.779 |
| pontoporeia_femorata |
0.00877 |
0.0404 |
0.217 |
0 |
0.132 |
0.789 |
| bipalponephtys_neotena |
0.00836 |
0.037 |
0.226 |
0 |
0.106 |
0.798 |
| maldanidae_spp |
0.00825 |
0.0272 |
0.303 |
0.0908 |
0.0322 |
0.807 |
| pagurus_pubescens |
0.00766 |
0.0231 |
0.331 |
0.0753 |
0.0161 |
0.815 |
| polynoidae_spp |
0.00756 |
0.0217 |
0.349 |
0.021 |
0.0952 |
0.823 |
| ampharete_oculata |
0.00725 |
0.0439 |
0.165 |
0.0666 |
0 |
0.831 |
| phyllodoce_mucosa |
0.00643 |
0.0241 |
0.267 |
0 |
0.106 |
0.838 |
| phyllodocidae_spp |
0.00629 |
0.0211 |
0.298 |
0.021 |
0.0484 |
0.845 |
| phoxocephalus_holbolli |
0.00621 |
0.0329 |
0.189 |
0 |
0.0827 |
0.851 |
| testudinalia_testudinalis |
0.00576 |
0.026 |
0.222 |
0.08 |
0 |
0.858 |
| harpinia_propinqua |
0.00547 |
0.0253 |
0.216 |
0.0753 |
0.0161 |
0.864 |
| quasimelita_formosa |
0.00486 |
0.0192 |
0.253 |
0 |
0.0739 |
0.869 |
| nephtys_ciliata |
0.00455 |
0.0213 |
0.214 |
0 |
0.0645 |
0.874 |
| platyhelminthes |
0.00429 |
0.0164 |
0.262 |
0 |
0.0484 |
0.878 |
| lacuna_vincta |
0.00427 |
0.0233 |
0.184 |
0 |
0.0417 |
0.883 |
| cancer_irroratus |
0.00405 |
0.0143 |
0.283 |
0.042 |
0.0161 |
0.887 |
| nephtys_incisa |
0.00399 |
0.0185 |
0.216 |
0.021 |
0.0161 |
0.892 |
| arrhoges_occidentalis |
0.00398 |
0.0167 |
0.239 |
0.0543 |
0 |
0.896 |
3. Regressions
3.1. Data manipulation
For the following analyses, independant variables are
habitat parameters and heavy metal concentrations, dependant
variables are diversity indices. Variables have been standardized
by mean and standard-deviation.
3.1.1. Identification of outliers
To identify stations that are not consistent with the others, we used
the multivariate Cook’s Distance (CD) on the uncorrelated variables. A
significative threshold of 4 times the mean of CD has been
established.
Design 1
We identified stations 60, 72, 80 and 96 as general
outliers. They have been deleted for the following analyses of Design
1.

Design 2
We identified stations 108 and 110 as general
outliers. They have been deleted for the following analyses of Design
2.

3.1.2. Correlations between predictors
Correlations have been calculated with Spearman’s rank
coefficient.
Design 1
According to these results, the following variables are highly
correlated (\(|\rho|\) > 0.80) so
they have been considered together in the regressions of Design 1:
- silt and clay (clay deleted)
We decided to keep sand, even if it is correlated with om, to stay
consistant with the 2014 campaign.
Correlation coefficients between habitat parameters (Design
1)
| om |
1 |
-0.068 |
-0.807 |
0.714 |
0.706 |
| gravel |
-0.068 |
1 |
-0.192 |
-0.37 |
-0.329 |
| sand |
-0.807 |
-0.192 |
1 |
-0.772 |
-0.768 |
| silt |
0.714 |
-0.37 |
-0.772 |
1 |
0.973 |
| clay |
0.706 |
-0.329 |
-0.768 |
0.973 |
1 |


Design 2
According to these results, the following variables are highly
correlated (\(|\rho|\) > 0.80) so
they have been considered together in the regressions of Design 2:
- cadmium and manganese (manganese deleted)
- copper, lead and zinc (copper and zinc
deleted)
We decided to keep arsenic, even though it is correlated with the
copper/lead/zinc group, to stay consistant with the 2014 campaign.
Correlation coefficients between heavy metals concentrations
(Design 2)
| arsenic |
1 |
0.492 |
0.736 |
0.876 |
0.773 |
0.399 |
0.646 |
0.816 |
0.903 |
| cadmium |
0.492 |
1 |
0.757 |
0.41 |
0.766 |
0.881 |
0.154 |
0.708 |
0.663 |
| chromium |
0.736 |
0.757 |
1 |
0.712 |
0.825 |
0.767 |
0.463 |
0.85 |
0.879 |
| copper |
0.876 |
0.41 |
0.712 |
1 |
0.633 |
0.38 |
0.572 |
0.829 |
0.89 |
| iron |
0.773 |
0.766 |
0.825 |
0.633 |
1 |
0.755 |
0.429 |
0.745 |
0.842 |
| manganese |
0.399 |
0.881 |
0.767 |
0.38 |
0.755 |
1 |
0.105 |
0.584 |
0.628 |
| mercury |
0.646 |
0.154 |
0.463 |
0.572 |
0.429 |
0.105 |
1 |
0.627 |
0.545 |
| lead |
0.816 |
0.708 |
0.85 |
0.829 |
0.745 |
0.584 |
0.627 |
1 |
0.898 |
| zinc |
0.903 |
0.663 |
0.879 |
0.89 |
0.842 |
0.628 |
0.545 |
0.898 |
1 |


3.2. Univariate regressions
We used linear models for the regressions on diversity indices.
Outliers and correlated variables were removed from these analyses.
Variables have been standardized by mean and standard-deviation
(coefficients need to be back-transformed to be used in predictive
models).
3.2.1. Simple regressions
These analyses have been do to explore the relationships between
variables. As it is a huge number of results to interpret, only multiple
regressions will be included in the article (see below).
Depth has been shown important for several parameters in the ANCOVAs,
so here are the corresponding scatterplots.

Design 1
Adjusted R-squared of simple regressions for Design 1
| S |
0.09824 |
0.06215 |
0.0708 |
0.1258 |
| N |
0.01242 |
0.01491 |
0.03477 |
0.03467 |
| H |
0.09519 |
0.03329 |
0.06053 |
0.1134 |
| J |
0.004809 |
-0.0122 |
0.01178 |
0.01984 |
p-values of simple regressions for Design 1
| S |
0.00425 |
0.01962 |
0.01359 |
0.001309 |
| N |
0.1732 |
0.1542 |
0.06343 |
0.06371 |
| H |
0.004839 |
0.06765 |
0.02101 |
0.002229 |
| J |
0.2504 |
0.7054 |
0.1785 |
0.123 |
Design 2
Adjusted R-squared of simple regressions for Design 2
| S |
-0.01268 |
-0.04896 |
-0.03331 |
-0.04823 |
-0.047 |
0.06622 |
| N |
0.008407 |
-0.04909 |
-0.03615 |
-0.04682 |
-0.04877 |
0.03425 |
| H |
-0.01205 |
-0.03027 |
-0.001362 |
-0.02749 |
-0.02325 |
0.102 |
| J |
-0.04952 |
-0.01768 |
-0.0304 |
-0.03285 |
-0.03656 |
-0.04851 |
p-values of simple regressions for Design 2
| S |
0.4008 |
0.8897 |
0.5762 |
0.8559 |
0.8132 |
0.1303 |
| N |
0.2907 |
0.8964 |
0.6107 |
0.8078 |
0.8796 |
0.2014 |
| H |
0.3967 |
0.543 |
0.3361 |
0.5155 |
0.478 |
0.08065 |
| J |
0.9251 |
0.4348 |
0.5443 |
0.5708 |
0.6162 |
0.8677 |
3.2.2. Multiple regressions
This section presents analyses done to determine which variables are
the most important to explain the parameters.
We identified which variables were selected after an AIC procedure to
predict the best the parameters. Results of the variable selection,
according to AIC, and details of the regressions, with diagnostics and
cross-validation, are summarized below.
Design 1
| om |
|
|
|
|
| gravel |
|
- |
+ |
|
| sand |
+ |
- |
+ |
|
| silt/clay |
+ |
- |
+ |
+ |
| Adjusted \(R^{2}\) |
0.17 |
0.1 |
0.18 |
0.02 |
Richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ om + gravel + sand + silt
| (Intercept) |
-0.07118 |
0.1104 |
-0.6444 |
0.5215 |
|
| om |
-0.03253 |
0.2084 |
-0.1561 |
0.8765 |
|
| gravel |
0.1478 |
0.3643 |
0.4057 |
0.6863 |
|
| sand |
1.23 |
0.9469 |
1.3 |
0.1982 |
|
| silt |
1.498 |
0.9953 |
1.505 |
0.137 |
|
## RMSE from cross-validation: 0.8980579
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sand + silt
| (Intercept) |
-0.06123 |
0.1061 |
-0.5769 |
0.5659 |
|
| sand |
0.8883 |
0.4034 |
2.202 |
0.03102 |
* |
| silt |
1.143 |
0.371 |
3.081 |
0.002963 |
* * |
## RMSE from cross-validation: 0.8688591
Variance Inflation Factors
| VIF |
3.55 |
3.55 |

Density
## FULL MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ om + gravel + sand + silt
| (Intercept) |
0.08685 |
0.1204 |
0.7216 |
0.473 |
|
| om |
0.25 |
0.2271 |
1.101 |
0.2749 |
|
| gravel |
-1.125 |
0.397 |
-2.833 |
0.006085 |
* * |
| sand |
-2.733 |
1.032 |
-2.649 |
0.01006 |
* |
| silt |
-2.591 |
1.085 |
-2.389 |
0.01974 |
* |
## RMSE from cross-validation: 1.185244
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ gravel + sand + silt
| (Intercept) |
0.06937 |
0.1195 |
0.5805 |
0.5635 |
|
| gravel |
-0.9239 |
0.3531 |
-2.616 |
0.01094 |
* |
| sand |
-2.179 |
0.902 |
-2.416 |
0.0184 |
* |
| silt |
-1.858 |
0.8575 |
-2.166 |
0.03379 |
* |
## RMSE from cross-validation: 1.161344
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Diversity
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ om + gravel + sand + silt
| (Intercept) |
-0.08487 |
0.111 |
-0.7647 |
0.4471 |
|
| om |
-0.1244 |
0.2094 |
-0.5938 |
0.5546 |
|
| gravel |
0.5965 |
0.3661 |
1.63 |
0.1079 |
|
| sand |
2.308 |
0.9514 |
2.426 |
0.01798 |
* |
| silt |
2.593 |
1 |
2.593 |
0.01168 |
* |
## RMSE from cross-validation: 0.8715363
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ gravel + sand + silt
| (Intercept) |
-0.07617 |
0.1095 |
-0.6957 |
0.489 |
|
| gravel |
0.4966 |
0.3235 |
1.535 |
0.1295 |
|
| sand |
2.032 |
0.8265 |
2.459 |
0.01649 |
* |
| silt |
2.228 |
0.7857 |
2.836 |
0.006011 |
* * |
## RMSE from cross-validation: 0.8738424
Variance Inflation Factors
| VIF |
2.09 |
7.18 |
7.42 |

Evenness
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ om + gravel + sand + silt
| (Intercept) |
-0.006695 |
0.1222 |
-0.05477 |
0.9565 |
|
| om |
-0.1766 |
0.2307 |
-0.7655 |
0.4467 |
|
| gravel |
0.445 |
0.4032 |
1.104 |
0.2737 |
|
| sand |
1.277 |
1.048 |
1.219 |
0.2273 |
|
| silt |
1.533 |
1.102 |
1.391 |
0.1688 |
|
## RMSE from cross-validation: 1.022129
Variance Inflation Factors
| VIF |
2.01 |
2.35 |
8.23 |
9.4 |

## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ silt
| (Intercept) |
0.03868 |
0.1141 |
0.339 |
0.7356 |
|
| silt |
0.1809 |
0.1159 |
1.561 |
0.123 |
|
## RMSE from cross-validation: 0.9675419
Variance Inflation Factors
| VIF |
1 |

Design 2
| arsenic |
|
|
|
|
| cadmium/manganese |
|
|
|
|
| chromium |
- |
- |
- |
|
| iron |
|
|
|
|
| mercury |
|
|
|
|
| lead/copper/zinc |
+ |
+ |
+ |
|
| Adjusted \(R^{2}\) |
0.29 |
0.16 |
0.21 |
0 |
Richness
## FULL MODEL
## Adjusted R2 is: 0.23
Fitting linear model: S ~ arsenic + cadmium + chromium + iron +
mercury + lead
| (Intercept) |
0.2151 |
0.1867 |
1.152 |
0.2674 |
|
| arsenic |
-0.09907 |
0.3912 |
-0.2532 |
0.8035 |
|
| cadmium |
-0.06645 |
0.352 |
-0.1888 |
0.8528 |
|
| chromium |
-1.191 |
0.8019 |
-1.486 |
0.1581 |
|
| iron |
-0.456 |
0.5699 |
-0.8002 |
0.4361 |
|
| mercury |
-0.2986 |
0.2195 |
-1.361 |
0.1937 |
|
| lead |
2.031 |
0.6547 |
3.103 |
0.007277 |
* * |
## RMSE from cross-validation: 1.015118
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.29
Fitting linear model: S ~ chromium + lead
| (Intercept) |
0.1818 |
0.1772 |
1.026 |
0.3177 |
|
| chromium |
-1.538 |
0.5749 |
-2.675 |
0.01499 |
* |
| lead |
1.655 |
0.5249 |
3.153 |
0.005237 |
* * |
## RMSE from cross-validation: 0.8191663
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Density
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: N ~ arsenic + cadmium + chromium + iron +
mercury + lead
| (Intercept) |
0.1452 |
0.2174 |
0.6681 |
0.5142 |
|
| arsenic |
0.2008 |
0.4553 |
0.4409 |
0.6656 |
|
| cadmium |
0.05286 |
0.4098 |
0.129 |
0.8991 |
|
| chromium |
-1.021 |
0.9333 |
-1.094 |
0.2912 |
|
| iron |
-0.5391 |
0.6633 |
-0.8128 |
0.429 |
|
| mercury |
-0.2339 |
0.2554 |
-0.9155 |
0.3744 |
|
| lead |
1.531 |
0.762 |
2.009 |
0.06288 |
|
## RMSE from cross-validation: 1.361962
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: N ~ chromium + lead
| (Intercept) |
0.1392 |
0.1995 |
0.698 |
0.4936 |
|
| chromium |
-1.293 |
0.6472 |
-1.998 |
0.06024 |
|
| lead |
1.407 |
0.5909 |
2.381 |
0.0279 |
* |
## RMSE from cross-validation: 0.9483052
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Diversity
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + iron +
mercury + lead
| (Intercept) |
0.2134 |
0.205 |
1.041 |
0.3143 |
|
| arsenic |
-0.2515 |
0.4294 |
-0.5857 |
0.5668 |
|
| cadmium |
-0.04641 |
0.3864 |
-0.1201 |
0.906 |
|
| chromium |
-0.9912 |
0.8802 |
-1.126 |
0.2778 |
|
| iron |
-0.1605 |
0.6255 |
-0.2566 |
0.801 |
|
| mercury |
-0.1535 |
0.2409 |
-0.6373 |
0.5335 |
|
| lead |
1.71 |
0.7186 |
2.379 |
0.03107 |
* |
## RMSE from cross-validation: 0.8743848
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0.21
Fitting linear model: H ~ chromium + lead
| (Intercept) |
0.1817 |
0.1848 |
0.9832 |
0.3379 |
|
| chromium |
-1.15 |
0.5997 |
-1.918 |
0.07024 |
|
| lead |
1.373 |
0.5476 |
2.508 |
0.02137 |
* |
## RMSE from cross-validation: 0.8518271
Variance Inflation Factors
| VIF |
2.7 |
2.7 |

Evenness
## FULL MODEL
## Adjusted R2 is: -0.23
Fitting linear model: J ~ arsenic + cadmium + chromium + iron +
mercury + lead
| (Intercept) |
-0.06755 |
0.2492 |
-0.271 |
0.7901 |
|
| arsenic |
-0.1468 |
0.5221 |
-0.2811 |
0.7825 |
|
| cadmium |
0.1053 |
0.4698 |
0.2242 |
0.8256 |
|
| chromium |
0.7817 |
1.07 |
0.7304 |
0.4764 |
|
| iron |
0.1713 |
0.7605 |
0.2252 |
0.8248 |
|
| mercury |
0.2131 |
0.2929 |
0.7275 |
0.4781 |
|
| lead |
-0.8121 |
0.8737 |
-0.9295 |
0.3674 |
|
## RMSE from cross-validation: 1.327671
Variance Inflation Factors
| VIF |
2.19 |
1.86 |
3.63 |
2.85 |
1.21 |
3.25 |

## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
| (Intercept) |
-0.04179 |
0.2191 |
-0.1908 |
0.8506 |
|
## RMSE from cross-validation: 1.05646
Quitting from lines 415-417 (C1_analyses_16B.Rmd) Erreur dans
Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : Il y a eu 26
avis (utilisez warnings() pour les
visionner)
3.3. Multivariate regressions
Independant variables are habitat parameters or heavy metal
concentrations, dependant variables are species abundances.
Variables have been standardized by mean and standard-deviation, and
outliers and correlated variables have been excluded. Taxon densities
were (log+1) transformed.
This analysis has been done on PRIMER, with a DistLM to identify the
variables that explain the most the community variability and with a
dbRDA to plot the results.
Design 1

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.08.
Design 2

Variables selected by the DistLM procedure have a \(R^{2}\) of 0.27.
🔝
Taxon densities were (log+1) transformed.